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Digital image processing-computer vision (DIP-CV) systems are used to automate malaria diagnosis through microscopy analysis of thin blood smears. Some variability is observed in the experimental design to evaluate the statistical measures of performance (SMP) of such systems. The objective of this work is assessing good practices when using SMP to evaluate DIP-CV systems for malaria diagnosis. A mathematical model was built to characterize diagnosis using DIP-CV systems and used to obtain curve families showing the relationships among various SMP of these systems, both using theoretical equations and computer simulation. Curve families showing (a) the relationships among the minimum number of positive erythrocytes (RBCs) to be observed, the per object (RBC) sensitivity and the probability to detect at least one positive, (b) per specimen sensitivity vs. total number of RBCs observed for a typical per object sensitivity and a range of parasite densities (c) per object positive predictive value vs. per object specificity for a typical per object sensitivity and various parasite densities. When determining the per specimen sensitivity, the parasite density p showed to have more influence on the number of RBCs that must be analyzed than the per object sensitivity. Measuring p accurately depends heavily upon the per object positive predictive value of the classifier. For low p values, this would require very high per object specificity and a high enough value of observed RBCs to measure this accurately.

Background: Immune checkpoint inhibitors (ICI) improve overall survival (OS) in patients with locally advanced, unresectable, or metastatic urothelial carcinoma (aUC), but response rates can be modest. We compared outcomes between patients with and without prior intravesical Bacillus Calmette-Guerin (BCG), who received ICI for aUC, hypothesizing that prior intravesical BCG would be associated with worse outcomes. Patients and Methods: We performed a retrospective cohort study across 25 institutions in US and Europe. We compared observed response rate (ORR) using logistic regression; progression-free survival (PFS) and OS using Kaplan-Meier and Cox proportional hazards. Analyses were stratified by treatment line (first line/salvage) and included multivariable models adjusting for known prognostic factors. Results: A total of 1026 patients with aUC were identified; 614, 617, and 638 were included in ORR, OS, PFS analyses, respectively. Overall, 150 pts had history of prior intravesical BCG treatment. ORR to ICI was similar between those with and without prior intravesical BCG exposure in both first line and salvage settings (adjusted odds radios 0.55 [P= .08] and 1.65 [P= .12]). OS (adjusted hazard ratios 1.05 [P= .79] and 1.13 [P= .49]) and PFS (adjusted hazard ratios 1.12 [P= .55] and 0.87 [P= .39]) were similar between those with and without intravesical BCG exposure in first line and salvage settings. Conclusion: Prior intravesical BCG was not associated with differences in response and survival in patients with aUC treated with ICI. Limitations include retrospective nature, lack of randomization, presence of selection and confounding biases. This study provides important preliminary data that prior intravesical BCG exposure may not impact ICI efficacy in aUC. © 2021

Offshore exploration requires the evaluation of hydrocarbon presence, estimation of volumes in place, and flow potential. To this capacity, formation testers are widely used to determine static data such as reservoir fluid gradients and reservoir pressure, obtain fluid samples, and to assess reservoir connectivity. Dynamic data, acquired with interval pressure transient testing and well testing techniques, are used to assess reserves and productivity. However, these evaluation techniques provide dynamic data at different resolution and length scales, and with different environmental footprint, cost, and operational constraints. A new wireline formation testing technique known as deep transient testing (DTT) has been introduced, which combines high-resolution measurements, higher flow rates, and longer test durations to perform transient tests in higher permeability, thicker formation, and at greater depth of investigation than with previous formation testers—without flaring and at a low carbon footprint. The platform combines advanced metrology with extensive automation to generate unique, real-time reservoir insights. Traditionally, pressure transient analysis and well deliverability predictions were produced through an analytical framework. Today, deep transient testing measurements are interpreted, and placed in reservoir context, in real-time by integration with geological and reservoir models. These steps can be performed from any wellsite utilizing cloud-based resources. Products such as reservoir fluid compressibility, saturation pressure, equation of state (EOS) models, well productivity, or minimum connected volumes are integrated in real-time interpretation utilizing numerical analysis. The digital infrastructure enables key reservoir insights to be shared between all stakeholders in a transparent and collaborative environment for both operational control and rapid decision making. This paper presents a case study where the new DTT technique was combined with numerical analysis and real-time integrated workflows to characterize a multilayer reservoir in a recent discovery in deepwater Mexico. During the drawdown phase of the DTT operation, real-time downhole fluid analysis was used to determine the fluid composition, density, viscosity, compressibility, and saturation pressure. These fluid properties were then used to generate and tune an EOS model. Accurate drawdown flow rate measurements and the subsequent pressure transients were combined with the fluid model and geologic model to enable integrated pressure transient history matching. The resulting calibrated numerical model honors the fluid measurements and geologic model and was used to predict the permeability profile, zonal producibility, and the volume of influence of the test.

Este artículo resalta la adecuada y justa relación entre algunas tradiciones y los llamados lapsos críticos o tiempos de alteración genérica de la naturaleza; además, se enfatiza en los espacios y tiempos de las tradiciones basadas en hechos o eventos verdaderos sin alterar los contextos históricos y más bien enriqueciéndolos. En este caso, las tradiciones se convierten en una fuente histórica muy especial porque muestran sutilezas difíciles de alcanzar en otros testimonios históricos. El ordenamiento cronológico de eventos naturales catastróficos registrados en las tradiciones enriquece el cuadro general de los desastres en el Perú, desde el tiempo en que gobernaba el inca Pachacútec hasta tiempos en que vivió Palma. Gracias a las tradiciones, el historiador puede ver los hechos, acontecimientos y estructuras con sus rumores, colores, alegrías, llantos y quebrantos.

The fast thermal and electromagnetic transients that occur in a superconducting magnet in case of a quench have the potential of generating large mechanical stresses both in the superconducting coils and in the magnet structure. While the investigation of such quench loads should generally be conducted to ensure a safe operation of the system, its importance is greatly enlarged in the case of high-field magnets based on strain sensitive superconductors. For these, a rigorous analysis of the magnet mechanics during a quench becomes critical. The scope of this work is hence to bring, for the first time, a detailed understanding of the three-dimensional mechanical behavior of a Nb3Sn accelerator magnet during a quench discharge. The study relies on the use of finite element models, where various multi-domain simulations are employed together to solve the coupled physics of the problem. Our analysis elaborates on the case study of the new MQXF quadrupole magnet, currently being developed for the high-luminosity upgrade of the LHC. Notably, we could find a very good agreement between the results of the simulation and experimental data from full-scale magnet tests. The validated model confirms the appearance of new peak stresses in the superconducting coils. An increase in the most relevant transverse coil stresses of 20–40 MPa with respect to the values after magnet cool-down has been found for the examined case. Peer Reviewed "Article signat per 13 autors/es: J Ferradas Troitino, H Bajas, L Bianchi, B Castaldo, P Ferracin, M Guinchard, S Izquierdo, J V Lorenzo, F Mangiarotti, J C Perez, E Takala, G Vallone and C Senatore"

This paper addresses two strategies for pattern recognition in high-dimension data sets, obtained from databases of digitally traced neurons. The first strategy has as distinctive characteristic that the features are obtained both from the whole neuron and from the axon and the dendrites as well. On the other hand, the second strategy is based in feature extraction from data sequences obtained from the decomposition of the traced neuron. Both strategies generate a wide variety of data which result in highly dimensional data sets. Two supervised pattern recognition alternatives were implemented for feature selection. When using the first strategy, the percentage of correctly classified cases raised up to 94.55% once the fusion of data extraction alternatives is performed and the feature selection methods are applied. These results are maintained for three different sets of traced neurons pertaining different regions of the cerebral cortex. By means of the second strategy, a group or recursive feature elimination alternatives was evaluated. Using this strategy, the mean percentage of correctly classified cases achieved was 78.5% and 76.79% which are comparable to those obtained using multiple iterations and the computational time was reduced three to four times. The results obtained encourage the use of the proposed strategies to reduce the dimensionality of the data sets obtained from the traced neurons.

Background This article addresses the automatic classification of reconstructed neurons through their morphological features. The purpose was to extend the capabilities of the L-Measure software. Methods New morphological features were developed, based on modifications of the conventional Sholl analysis. The lengths of the compartments, as well as their volumes, were added to the features used in the classical analysis in order to improve the results during automatic neuron classification. FSM were used to obtain subsets of lower cardinality from the full feature sets and the usefulness of these subsets was tested through their use in supervised classification tasks. The study was based on two types of neurons belonging to mice: pyramidal and GABAergic interneurons. Furthermore, a set of pyramidal neurons belonging to Later 4 and Layer 5 was analyzed. Results RF classifier shown the best performance combined with a Wrapper method.U-WNAD set allowed to obtain higher values than WN, A and D in all cases and better results than LM for the filters and wrappers FSM. U-LM-WNAD set, led to the highest AUC values for all the FSM studied. Similar results for different regions of cortex were obtained. Comparison with Existing Methods The new features exhibited high discriminatory power with which the values of AUC and Acc obtained in the experiments exceeded those obtained using only the features provided by L-Measure. Conclusions The highest values of AUC and Acc were obtained from the sets U-WNAD and U-LM-WNAD, evidencing the discriminatory power of the new proposed features.

Inflammatory markers have prognostic value in various tumors due to the role of inflammatory phenomena at different stages of tumor development. The aim of this study is to demonstrate the prognostic value of these markers, as well as other clinical and analytical variables in patients with metastatic castration-resistant prostate cancer (mCRPC).Prospective cohort study carried out on 80 patients diagnosed with mCRPC. Clinical and analytical data were collected, and the following inflammatory markers were estimated: Absolute Neutrophil Count (ANC), Neutrophil-Lymphocyte Ratio (NLR), Total Platelet Count (TPC), Platelet-Lymphocyte Ratio (PLR), Lymphocyte-Monocyte Ratio (LMR) and Systemic Inflammation Index (SII). The values of albumin, hemoglobin (Hb), alkaline phosphatase (ALP) and lactate dehydrogenase (LDH) were also determined.Patients with ANC7500, NLR3, PLR150, LMR3 and/or SII535,000, presented significantly lower median survival time than the remaining patients, and TPC was the only marker which did not show a significant association. Moreover, NLR, PLR and SII were inversely correlated with survival time. Patients with hypoalbuminemia, anemia, and elevated LDH values had significantly lower median survival time. Albumin and hemoglobin were directly correlated to overall survival time. The need for analgesia was also associated with shorter survival.The values of certain inflammatory markers are associated with shorter survival time in patients with mCRPC, and their use in clinical practice can be considered to evaluate the prognosis and estimate survival.

The development of antimalarial drugs requires performing laboratory experiments that include the analysis of blood smears infected with Plasmodium. Analyzing visually the resulting microscopy images is usually a slow and tedious task prone to errors due to fatigue and subjectivity of the analysts. These facts have motivated the creation of digital image processing systems to automate this analysis. In this work a computer vision solution to process microscopy images of blood smears containing erythrocytes infected with Plasmodium is shown. This system performs tasks like illumination and color correction, image segmentation including splitting of clumped objects and extraction of color features. A set of different classifiers was tested and evaluated to find the best one in terms of indexes of effectiveness. A new feature named pixels fraction was introduced and used together with a number of other color-related features, from which a subset to classify cells into normal or infected was selected. The classifiers evaluated were: support vector machines (SVM), K-nearest neighbors (KNN), J48, Random Forest (RF), Naive Bayes and linear discriminant analysis (LDA). All of them were evaluated in terms of correct classification rate, sensitivity, specificity, F-measure and area under Receiver Operating Characteristic (ROC) curve (AUC). The effectiveness of the pixels fraction as a new feature was demonstrated by the experimental results. In regard to classifiers, J48 and Random Forest showed the best results.

A large number of training quenches at various currents, temperatures, and ramp rates, have been performed on six 11 T dipole model magnets. Quenches in the midplane of these magnets were of special interest, since the quench current in the last three models measured in 2016 was limited to between 84% and 92% of the magnets short sample limit. Measurements of quench propagation velocity, based on both voltage taps and quench antennas, yield a high propagation velocity of 50 to 80 m/s. Due to the high magnetic field gradient over the width of the midplane turn such a high propagation speed cannot be explained by propagation in longitudinal direction of the strand following the twist pitch. In these cases, current and heat sharing at the thin cable edge (where the field, stress, and cable compaction are high) are likely to provoke strand-to-strand quench propagation at higher velocities than along the strands. This investigation is focused on analyzing the quench propagation along the strands and strand-to-strand of various measured cases.